The Analysis

16 Aug

As a consultant for the past five years, I’ve had the opportunity to run several large data projects for businesses in multiple industries. Usually the request is to help them make better business decisions based on the advanced analytics they will get from bigger, better, faster data. However, once the project starts and business interviews and process mapping begins, the biggest and most immediate improvement almost always comes from updating bad processes, not necessarily the data.

Below I present two projects that were presupposed to be data problems, when in fact they were process problems. They highlight both the challenges internally and what steps needed to be taken to be improved.

Project 1 – Education Industry

Disparate processes led to disparate data

In a request for better predictive analytics to indicate cheating in real-time, I uncovered the following process problem:

Multiple people were transforming a dead data set in order to run analytics on it, and then storing it as another dead data set.

What I mean by ‘dead data set’ is that a flat file would come in the doors, four different analytics departments would transform it into a SAS data set they could use, store the SAS data set on the server, and then run analytics to produce ANOTHER SAS data set. These were time-based files, and didn’t include previous periods.

Their problem was multi-faceted:

The process took too long and couldn’t be put into production for real-time data streams,

The files produced were extremely disparate, and

Files weren’t named based on a common protocol, so they were hard to find if you weren’t the one naming the file.

The solution

Better data process led to better data and analysis

Instead of doing a manual ETL (Extract, Transform, Load) process when the data came in, the files were added to a data lake via automated Load process. A process was added within the data lake to transform the data and combine it for a singular view of all the data. SAS was set up within the data lake environment (instead of locally on individual analyst’s computers), and therefore analysis could be run much faster and across all data. The analysis produced was then saved according to naming protocols so others could access as well.

Not only did the analysts get bigger, badder, and better analysis capabilities, but the time it took to get the analysis completed dropped from a week to hours. Plus, more analysts could spend more time actually running analysis as opposed to doing manual ETL processes. Most importantly, the data was no longer ‘dead’ – it was very much alive and ready for use.

Project 2 – Insurance Industry

A similar request for better predictive analytics came from a large insurance agency. They wanted to better understand the impact of their agents and underwriters, which types of policies were the most profitable, and how much risk they could take on without losing money.

Acting as a BA for project initiation, I had the opportunity to talk with multiple people in all areas of the business, map the applications used, and diagram how the applications fit together. When this was completed, I ended up with a process diagram that was an absolute mess! Everything seemed hacked together in a flow that didn’t make logical sense.

The underlying reason things were this bad was because they created their ‘digital’ process based on the paper process they used before computers. As in, they did all their selling, underwriting, and communications based on the process from 1985: A piece of mail comes into the mail room, it’s opened and sent to someone’s desk, the person writes notes on the piece of paper, the piece of paper is then sent over for review, the piece of paper is approved and sent to the financial department to be issued, and a piece of paper is mailed back to the requestor.

But now they did this same process via Outlook and saved PDF files. Yes, you read that correctly. The PDF files were saved in a repository, and passed around via email.

The solution

They didn’t implement the solution, which was to tear everything down and start fresh. I recommended they use a cloud-based data server with applications for the agents to submit data. The data could then be accessed by underwriters, who could use a simple algorithm to determine risk and assign a score to the record. The data could then be accessed by the financial team to implement the policy and resulting transactions. Only THEN could they start doing analysis on how the risk algorithm score could be optimized.

However, that was too expensive, so they decided to stay the current course and try and do risk analytics off of a small subset of data that had been stored digitally.

Final thoughts

It’s easy for the C-Suite to call down from the executive offices for better predictive analytics, but almost impossible to achieve them without dedicated process improvement. That takes money. BUT, that money will almost be immediately returned in the form of cost efficiencies, whether they’re personnel (ie Janet that is the only one that knows how to run this database), time-based (ie Jeff is the only one that can do that report, and he’s on vacation), or operational (ie we have four people on staff that are constantly fixing legacy systems).

Here are some immediate questions you can ask to determine if it’s a process problem:

Does it take too long for simple processes to get done?

Is it hard to find things, like reports or files?

Do I have a person in my organization that cannot get hit by a bus, or things will completely fall apart?

If you answered yes to one or more of these, you most certainly have a process problem. Fix the process, save money, and get better data and resulting analytics.

23 Jun

I am bombarded by thought leaders, business owners, and analysts asking for the biggest and the best of analytics. I’m talking Machine Learning, Customer 360 Experience, Attribution Modeling, Predictive Content, and on and on and on. What they DON’T ask for is “Marketing Analytics” because it’s apparently SO five years ago. I’m here to prove to you, it’s not. It’s very much alive and we have to make sure we build all these basics before we get to the AIs of the digital space.

The Curse of Being Trendy

Yes, we’re all in marketing. Yes, we are trendy. But good data and solid processes are NOT trendy. They aren’t sexy; they’re useful. And so because we get so bored with the basics, we blast through to the new and trendy. I’ve had clients tell me not to even mention the term “marketing analytics.” Yes, I’m serious.

But I’m here to pull back the curtain on what we’re all doing when we aren’t talking to each other about Machine Learning and the Customer 360: We’re googling “marketing analytics.” A LOT. Still.

In fact, we have been googling “marketing analytics” more over time, steadily increasing since 5 years ago.

Yes, We’re All Searching for the Basics

And it’s not just the biggest states doing all these searches. It’s Massachusetts. It’s Oregon. It’s (understandably) D.C. – especially in the past 90 days.

OK, So What?

This blog post won’t change the CEO’s mind about how cool Customer Journey Mapping is, but it can provide a little context on why basics are just as important. We’re all looking to improve the data we’re getting, and how to analyze it. That’s exactly what we do here, and that’s why we’ll continue talking about “Marketing Analytics” into the foreseeable future.

If you’d like to see for yourself what people are searching, check out Google Trends.

So your business is using or implementing marketing analytics, but you can’t seem to get it to ‘work’. You may have bought an expensive dashboard program, hired some incredibly smart analysts, and started collecting mountains of data. Yet none of these can be successful without the most integral part: MEANING.

All successful marketing analytics programs have three common elements:

Strategy

Analytics

Meaning

Strategy comes from your idea department, whether that’s your creative team, your marketing managers, or (and hopefully) a combination of the two. No marketing program can be successful without relevant ideas that engage the customer.

Analytics is the department mentioned in the opening paragraph, ie the team of analysts, the cool new reporting platforms, and the mounds of data that is created from marketing strategies and tactics.

Meaning is the most difficult to find, as this element has to be provided by a team that understands the complicated analysis, can interpret data into business needs, and can suggest next steps and potential impact. Without meaning, the time and money you’re investing in analytics can be, well … meaningless.

In order to get meaning from your strategy and analytics, look for a person or team that has both the expertise in the field of marketing, coupled with a deep understanding of data and analytics. Only then will your marketing analytics truly be successful.

Ready for Success?

Our strengths include taking complex ideas and presenting them simply. Are you ready to bridge the gap between strategy and analytics?

When setting up a marketing campaign or analyzing user experience, the first thing your team should do is create Key Performance Indicators, or KPIs, around the primary objective of the activity. These KPIs should be calculations that inform you how to optimize the activity, and not just ideal numbers you would like to hit.

For reference, we’ve put together some good KPIs and compared them to simple metrics in the infographic below. If your team would like help in both identifying and setting KPIs, we offer a one-day GoalSession to do just that.

Learn More About the GoalSession

Read more about what to expect during the one-day GoalSession, as well as the activities and collateral you will get once completed.

12 Mar

Too many times I’ve seen extremely valuable data in the hands of people that it probably shouldn’t be, especially in the digital marketing industry. I’m talking Google Analytics data, Adwords data, and other similar account data. This is terrifying because if a business allows individuals or agencies to store and control the data, then

The data collected won’t align with internal business needs,

The data can’t be accessed for any internal analysis (so kiss all that super-cool marketing analytics and user experience analysis good-bye), and most importantly

When the person/agency goes, the data is gone forever. Ouch!

Ask yourself this: Does your internal business department OWN the accounts being used to collect digital marketing data, or are you relying on others to create and manage them for you? If the answer is no, read on to learn WHY you should own the accounts, as well as best practices on how to set them up.

Background

Let’s set the baseline for this article by defining what I mean by “data.” If your company is currently running pay-per-click ads for search engine marketing, then your company has either created the content, links, and cookies for the ads. OR you have paid a marketing agency to set up the content, links, and metrics for the ads. All of that comprises your data, and you own all of it.

This means data does not ONLY refer to the numbers, but also the content of your marketing. In fact, data is considered anything that can be captured within a computing system. So the text in the ads is your data, not just the impressions and clicks.

Now Apply this Concept to Websites

There was a lot of debate about twenty years ago when companies started paying agencies to build websites – mostly around who ‘owned’ the website. After a lot of legal back-and-forth, there have been guidelines put in place around who owns what. Let’s walk through the website ownership example to help frame the issue at hand:

OWNED

Images – The images created for the website are the property of the business that paid for the design of, or bought the rights to use, the images.

Text – Any text created for the website is the property of the business that paid for said content.

HTML/CSS/Java – Coding done specifically for the website is the property of the business that paid for the website to be built.

NOT OWNED

Domain – This is complicated, but generally speaking domains are leased.

Content Management Systems – Again, these are usually leased and therefore owned by the purveyor of said CMS. Ie, WordPress owns its CMS; businesses use the infrastructure for their owned content.

This concept can be applied directly to digital marketing – MailChimp owns the system, the business owns the content. Google Adwords/DCM owns the serving platform, the business owns the content.

Taking it one step further, anything that is created FROM the content SHOULD be the property of the business. That is to say, if I own all the content on my website thedata.co, then I own all the resulting data. I own the number of visitors that came to my website; I own the number of times a button was clicked.

Who Cares?

With the absolute avalanche of digital marketing tactics over the past decade, agencies and business alike are scrambling to not just create the content, but learn how to track and store all the data it accrues. This often results in careless set up and limited access to both the accounts and the data.

For example, it’s common for a business to pay an agency to build a website. The agency will often add a Google Analytics account as the tracking device for the website, but use their internal the agency Google Analytics account. This means the data collected from the website is the primary property of the agency, not the business.

Another common occurrence is a business will set up their own Google Analytics, but use an employee email as the primary account owner. This can be very troublesome if that employee transitions away from the company.

Best Practices

A better way to collect the data you should already own is broken down below:

If you’re a business:

Set up a broad email account for the entire marketing department. This could be something like marketing@yourbusiness.com. Use this email address to set up all the digital marketing accounts and add access to employees as needed. This will ensure the data stays with the business and NOT with an employee.
If you are working with an agency to create and run ad campaigns, give them access to this broad email. This is crucial, especially if you ever need to switch agencies, since your data will stay intact – just the access will change.

If you’re an agency:

Do your best to prompt the business to provide you with a broad email address as stated above. If they do not have one, try to make a generic gmail account for them, such as TheirBusinessMarketing@gmail.com.

Agencies will also need a broad email account to obtain access to business accounts. For example, Accounts@OurAgency.com can be granted access to a business Google Analytics, or MailChimp, or similar. Again, this helps alleviate any account transition, or employee transition, that is bound to happen.

If all else fails, work with a professional that has enough experience to thoughtfully plan out marketing ad accounts so the data can be properly collected over the long term. And of course, we can help.

Final Takeaways

The data that your marketing accounts collect belongs to the business that has paid for said content. If you do not have access to this data, or are looking to start collecting it for analytics purposes, reach out and we can help you get there.